LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Ma, CY; Liu, JJ; Liu, T; Wang, XZ (2015)
Publisher: IEEE
Languages: English
Types: Other
Subjects:
Despite the availability of various Process Analytical Technologies (PAT) for measuring other particle properties, their inherit limitations for the measurement of crystal shape have been restricted. This has impacted, in turn, on the development and implementation of optimisation, monitoring and control of crystal shape and size distributions within particle formulation and processing systems In recent years, imaging systems have shown to be a very promising PAT technique for the measurement of crystal growth, but still essentially limited as a technique only to provide two-dimensional information. The idea of using two synchronized cameras to obtain 3D crystal shape was mentioned previously (Chem Eng Sci 63(5) 1171-1184, 2008) but no quantitative results were reported. In this paper, a methodology which can directly image the full three-dimensional shape of crystals has been developed. It is based on the mathematical principle that if the two-dimensional images of an object are obtained from two different angles, the full three-dimensional crystal shape can be reconstructed. A proof of concept study has been carried out to demonstrate the potentials in using the system for the three-dimensional measurement of crystals.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] K. R. Lee, Zuber, G., Katrincic, L., Chemometrics approach to the determination of polymorphism of a drug compound by infrared spectroscopy, Vol. 26, No. 2, 135-147, 2000.
    • [2] J. Calderon de Anda, X. Z. Wang, X. Lai, and K. J. Roberts, Classifying organic crystals via in-process image analysis and the use of monitoring charts to follow polymorphic and morphological changes, Journal of Process Control, Vol. 15, No. 7, 785-797, Oct, 2005.
    • [3] J. Calderon de Anda, X. Z. Wang, X. Lai, K. J. Roberts, K. H. Jennings, M. J. Wilkinson, D. Watson, and D. Roberts, Real-time product morphology monitoring in crystallization using imaging technique, AIChE Journal, Vol. 51, No. 5, 1406-1414, May, 2005.
    • [4] J. Calderon de Anda, X. Z. Wang, and K. J. Roberts, Multi-scale segmentation image analysis for the in-process monitoring of particle shape with batch crystallisers, Chemical Engineering Science, Vol. 60, No. 4, 1053-1065, Feb, 2005.
    • [5] M. Kempkes, J. Eggers, and M. Mazzotti, Measurement of particle size and shape byFBRMand in situ microscopy, Chemical Engineering Science, Vol. 63, No. 19, 4656-4675, 2008.
    • [6] Lasentec Inc, http://www.lasentec.com.
    • [7] R. F. Li, R. Penchev, V. Ramachandran, K. J. Roberts, X. Z. Wang, R. J. Tweedie, A. Prior, J. W. Gerritsen, and F. M. Hugen, Particle shape characterisation via image analysis: from laboratory studies to in-process measurements using an in situ particle viewer system, Organic Process Research & Development, Vol. 12, 837 849, Jun, 2008.
    • [8] R. F. Li, G. B. Thomson, G. White, X. Z. Wang, J. C. De Anda, and K. J. Roberts, Integration of crystal morphology modeling and on-line shape measurement, AIChE Journal, Vol. 52, No. 6, 2297-2305, Jun, 2006.
    • [9] MessTechnik Schwartz GmbH, http://www.mts-duesseldorf.de/.
    • [10] D. B. Patience, and J. B. Rawlings, Particle-shape monitoring and control in crystallization processes, AIChE Journal, Vol. 47, No. 9, 2125-2130, Sep, 2001.
    • [11] Perdix, http://www.perdix.nl/frames.html.
    • [12] H. Y. Qu, M. Louhi-Kultanen, and J. Kallas, In-line image analysis on the effects of additives in batch cooling crystallization, Journal of Crystal Growth, Vol. 289, No. 1, 286-294, Mar, 2006.
    • [13] J. Scholl, D. Bonalumi, L. Vicum, M. Mazzotti, and M. Muller, In situ monitoring and modeling of the solvent-mediated polymorphic transformation of L-glutamic acid, Crystal Growth & Design, Vol. 6, No. 4, 881-891, Apr, 2006.
    • [14] X. Z. Wang, J. Calderon De Anda, and K. J. Roberts, Real-time measurement of the growth rates of individual crystal facets using imaging and image analysis: a feasibility study on needle-shaped crystals of L-glutamic acid, Chemical Engineering Research & Design, Vol. 85A, 921-927, 2007.
    • [15] X. Z. Wang, J. Calderon De Anda, K. J. Roberts, R. F. Li, G. B. Thomson, and G. White, Advances in on-line monitoring and control of the morphological and polymorphic forms of organic crystals grown from solution (paper downloadable from www.kona.org.jp/html/about/index2005.html), KONA, Vol. 23, 69-85, 2005.
    • [16] X. Z. Wang, K. J. Roberts, and C. Y. Ma, Crystal growth measurement using 2D and 3D imaging and the perspectives for shape control, Chemical Engineering Science, Vol. 63, No. 5, 1171-1184, 2008.
    • [17] M. J. Wilkinson, Jennings, K. H., Hardy, M., Non-invasive video imaging for interrogating pharmaceutical crystallization processes, Microscopy and Microanalysis, Vol. 6, No. 2, 996-997, 2000.
    • [18] L. L. Simon, T. Merz, S. Dubuis, A. Lieb, and K. Hungerbuhler, In-situ monitoring of pharmaceutical and specialty chemicals crystallization processes using endoscopy-stroboscopy and multivariate image analysis, Chemical Engineering Research & Design, Vol. 90, 1847-1855, 2012.
    • [19] K. Patchigolla, and D. Wilkinson, Crystal shape characterization of dry samples using microscopic and dynamic image analysis, Particle & Particle Systems Characterization, Vol. 26, 171-178, 2009.
    • [20] C. Y. Ma, and X. Z. Wang, Model identification of crystal facet growth kinetics in morphological population balance modeling of Lglutamic acid crystallization and experimental validation, Chemical Engineering Science, Vol. 70, 22-30, 2012.
    • [21] S. N. Black, and D. L. Gray, Sensors and Science in Crystallisation of Pharmaceuticals, in 7th World Congress of Chem. Eng., Scotland, 2005.
    • [22] P. A. Larsen, J. B. Rawlings, and N. J. Ferrier, Model-based object recognition to measure crystal size and shape distributions from in situ video images, Chemical Engineering Science, Vol. 62, 1430 1441, 2007.
    • [23] M. J. Wilkinson, K. H. Jennings, R. Plant, R. Logan, and B. Drayson, Particle size and shape measured for process monitoring using high-speed image analysis, in Particulate Systems Analysis, Harrogate, UK, 2003.
    • [24] B. W. Reed, J. V. Hokanson, O. S. Hamann, and T. W. Montague, System for acquiring an image od a multi-phase fluid by measuring backscattered light, US, 1998.
    • [25] D. Kluitmann, F. H. Schwartz, and T. Zoeller, Device for determining the particle properties of particles contained in a fluid medium, especially the morphology, shape and size by use of illuminating light, CCD camera and appropriate filters to improve image contrast, Germany, 2002.
    • [26] J. W. Gerritsen, and G. J. Brinks, Inrichting en werkwijze voor het vastleggen van optische gegevens van een dispersie, The Netherlands, 2005.
    • [27] J. Eggers, M. Kempkes, J. Cornel, M. Mazzotti, I. Koschinski, and E. Verdurand, Monitoring size and shape during cooling crystallization of ascorbic acid, Chemical Engineering Science, Vol. 64, 163-171, 2009.
    • [28] S. Schorsch, T. Vetter, and M. Mazzotti, Measuring multidimensional particle size distributions during crystallization, Chemical Engineering Science, Vol. 77, 130-142, 2012.
    • [29] C. Y. Ma, J. Wan, and X. Z. Wang, Faceted growth rate estimation of potash alum crystals grown from solution in a hot-stage reactor, Powder Technology, Vol. 227, 96-103, 2012.
    • [30] C. Borchert, E. Temmel, H. Eisenschmidt, H. Lorenz, A. Seidel-Morgenstern, and K. Sundmacher, Image-based in situ identification of face specific crystal growth rates from crystal populations, Crystal Growth & Design, Vol. 14, 952-971, 2014.
    • [31] D. Gorpas, K. Politopoulos, and D. Yova, A binocular machine vision system for three-dimensional surface measurement of small objects, Computerized Medical Imaging and Graphics, Vol. 31, 625-637, 2007.
    • [32] M. Kempkes, T. Vetter, and M. Mazzotti, Measurement of 3D particle size distributions by stereoscopic imaging, Chemical Engineering Science, Vol. 65, 1362-1273, 2010.
    • [33] T. Helgason, J. Lee, M. Smith, A. T. Moeller, T. Thorgeirsson, V. Hofer, J. Pilz, and J. A. Benediktsson, Apparatus and method for analysis of size, form and angularity and for compositional analysis of mineral and rock particles, Canada, 2006.
    • [34] B. Bujak, and M. Bottlinger, Three-dimensional measurement of particle shapes, Particle & Particulate Systems Characterisation, Vol. 25, 293-297, 2008.
    • [35] S. M. Boersma, F. A. van den Heuvel, A. F. Cohen, and R. E. M. Scholtens, Photogrammetric wound measurement with a three-camera vision system, International Archives of Photogrammetry and Remote Sensing, Vol. 33, No. B5/1, 84-91, 2000.
    • [36] S. Nedevschi, C. Vancea, T. Marita, and T. Graf, Online extrinsic parameters calibration for stereovision systems used in far-range detection vehicle applications, IEEE Trans. Intell. Transp. Syst., Vol. 8, No. 4, 651-660, 2007.
    • [37] O. Schreer, P. Kauff, and T. Sikora, 3D Videocommunication: Algorithms, concepts and real-time systems in human centred communications, UK: John Wiley and Sons, 2005.
    • [38] G. H. Ballantyne, and F. Moll, The da Vinci telerobotic surgical system: the virtual operative field and telepresence surgery, Surgical Clinics of North America, Vol. 83, No. 6, 1293 -1304, 2003.
    • [39] S. E. Butner, and M. Ghodoussi, A real-time system for telesurgery, in Proceedings of International conference on distributed computing systems, USA, 2001, pp. 236-243.
    • [40] K. Hasegawa, and Y. Sato, Endoscope System for High-Speed 3D Measurement, Systems and Computers in Japan, Vol. 32, No. 8, 271-279, 1999.
    • [41] T. Hu, P. K. Allen, and D. L. Fowler, In-Vivo Pan/Tilt Endoscope with Integrated Light Source, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), USA, 2007.
    • [42] T. Hu, P. K. Allen, T. Nadkarni, N. J. Hogle, and D. L. Fowler, Insertable Stereoscopic 3D Surgical Imaging Device with Pan and Tilt, in Medicine Meets Virtual Reality (MMVR) 16, USA, 2008.
    • [43] U. D. A. Mueller-Richter, A. Limberger, P. Weber, K. W. Ruprecht, W. Spitzer, and M. Schilling, Possibilities and limitations of current stereo-endoscopy, Surg Endosc, Vol. 18, 942 947, 2004.
    • [44] D. Stoyanov, A. Darzi, and G. Z. Yang, A practical approach towards accurate dense 3D depth recovery for robotic laparoscopic surgery, Computer Aided Surgery, Vol. 10, No. 4, 199 208, 2005.
    • [45] N. Taffinder, S. G. T. Smith, J. Huber, R. C. G. Russell, and A. Darzi, The effect of a second-generation 3D endoscope on the laparoscopic precision of novices and experienced surgeons, Surg Endosc, Vol. 13, 1087 1092, 1999.
    • [46] M. R. Singh, J. Chakraborty, N. Nere, H.-H. Tung, S. Bordawekar, and D. Ramkrishna, Image-analysis-based method for 3D crystal morphology measurement and polymorph identification using confocal microscopy, Crystal Growth & Design, Vol. 12, 3735-3748, 2012.
    • [47] J. F. Cardenas-Garcia, H. G. Yao, and S. Zheng, 3D reconstruction of objects using stereo imaging, Optics and Lasers in Engineering, Vol. 22, 193-213, 1995.
    • [48] G. Karimian, A. A. Raie, and K. Faez, A new efficient stereo line segment matching algorithm based on more effective usage of the photometric, geometric and structural information, IEICE Trans. Inf. & Syst., Vol. E89-D, No. 7, 2012-2020, 2006.
    • [49] J. Canny, A computational approach to edge detection, IEEE Trans. Patt. Recog. and Mach. Intell, Vol. 36, 961 - 1005, 1986.
    • [50] R. C. Gonzalez, and R. E. Woods, Digital image processing, 2nd ed ed., Upper Saddle River: Prentice Hall, 2002.
    • [51] C. Harris, and M. Stephens, A combined corner and edge detector, in The 4th Alvey Vision Conference, 1988, pp. 147 152.
    • [52] R. Hartley, and A. Zisserman, Multiple view geometry in computer vision, Cambridge: Cambridge University Press, 2003.
    • [53] E. Trucco, and A. Verri, Introductory Techniques for 3-D Computer Vision, New Jersey: Prentice Hall, 1998.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article